#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr


import torch, torchvision, os
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp

def nerfw_loss(render, gt, init_beta, beta_min=0.03, iter=0):
    # shift minimal according to the essay
    beta = beta_min + init_beta
    loss_pix = torch.square(render - gt) / (2 * beta ** 2)
    # by def. log(β²)/2 = log(β)
    # NOTE: this term can be negative, so +3 to make loss positive, trick impl. borrowed from in nerf_pl and nerfstudio :(
    loss_reg = torch.log(beta) + 3
    loss = loss_pix + loss_reg
    
    look_up = 'look_up'
    os.makedirs(look_up, exist_ok=True)
    if iter % 100 == 0:
        torchvision.utils.save_image(torch.cat([init_beta.repeat(3,1,1), 
                                                beta.repeat(3,1,1), 
                                                loss_reg.repeat(3,1,1),
                                                torch.mean(loss_pix, dim=0, keepdim=True).repeat(3,1,1),
                                                torch.mean(loss, dim=0, keepdim=True).repeat(3,1,1),
                                                render, 
                                                gt], -1), os.path.join(look_up, f'{iter:05d}-{iter}.png'))
    return loss.mean()

def l1_loss(network_output, gt, mask=None):
    if mask is not None:
        return (torch.abs(network_output - gt) / mask).mean()
    return torch.abs(network_output - gt).mean()

def l2_loss(network_output, gt):
    return ((network_output - gt) ** 2).mean()

def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
    return gauss / gauss.sum()

def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
    return window

def ssim(img1, img2, window_size=11, size_average=True, mask=None):
    channel = img1.size(-3)
    window = create_window(window_size, channel)

    if img1.is_cuda:
        window = window.cuda(img1.get_device())
    window = window.type_as(img1)

    return _ssim(img1, img2, window, window_size, channel, size_average)

def _ssim(img1, img2, window, window_size, channel, size_average=True):
    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2

    C1 = 0.01 ** 2
    C2 = 0.03 ** 2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1).mean(1).mean(1)

